用智能代理和本地数据动态生成灾害应对区域，提升规划灵活性
Enhancing Demand-Oriented Regionalization with Agentic AI and Local Heterogeneous Data for Adaptation Planning
- 用自适应地理过滤与区域生长改进自组织映射模型
- 支持用户交互式生成并评估洪水风险区域，提升响应效率
- 适合城市规划、应急管理和地理信息系统的研究人员
传统规划单元（如普查区、邮政编码或社区）常无法反映本地实际需求，且缺乏应对灾害的灵活性。为支持动态规划单元的创建，本文提出一个基于智能代理的规划支持系统，结合人机协同原则，实现需求导向的灾害规划区域生成。该平台基于改进的代表性空间约束自组织映射（RepSC-SOM），引入自适应地理过滤与区域生长优化，使AI代理能推理、规划并执行任务，包括建议输入特征、引导空间约束和辅助交互探索。通过佛罗里达杰克逊维尔市洪灾风险案例验证，系统支持用户交互式生成、评估区域划分，兼顾计算严谨性与用户决策主导性。
原文摘要 · Abstract (English)
Conventional planning units or urban regions, such as census tracts, zip codes, or neighborhoods, often do not capture the specific demands of local communities and lack the flexibility to implement effective strategies for hazard prevention or response. To support the creation of dynamic planning units, we introduce a planning support system with agentic AI that enables users to generate demand-oriented regions for disaster planning, integrating the human-in-the-loop principle for transparency and adaptability. The platform is built on a representative initialized spatially constrained self-organizing map (RepSC-SOM), extending traditional SOM with adaptive geographic filtering and region-growing refinement, while AI agents can reason, plan, and act to guide the process by suggesting input features, guiding spatial constraints, and supporting interactive exploration. We demonstrate the capabilities of the platform through a case study on the flooding-related risk in Jacksonville, Florida, showing how it allows users to explore, generate, and evaluate regionalization interactively, combining computational rigor with user-driven decision making.
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